Dealing with data coming from a space-time inhomogeneous process,there is often the need of semi-parametric estimates of the conditional intensityfunction; isotropic or anisotropic multivariate kernel estimates can be used, with windows sizes h. The properties of the intensities estimated with this choice of hare not always good for specific fields of application; we could try to choose h inorder to have good predictive properties of the estimated intensity function. Since adirect ML approach cannot be followed, we propose an estimation procedure, computationally intensive, based on the subsequent increments of likelihood obtainedadding an observation at time. The first results obtained are very encouraging. Someapplication in statistical seismology is presented.
|Titolo della pubblicazione ospite||Classification and multivariate analysis for complex data structures.|
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2011|
|Nome||STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION|